A method for artifact suppression in a sensed signal includes receiving the sensed signal sensed in a brain of a patient, wherein the sensed signal includes a neural signal and artifacts from a cardiac signal, decomposing the sensed signal into a plurality of components of the sensed signal, determining a first group of components, from the plurality of components, that are correlated with one another, determining an estimate of the cardiac signal based on the first group of components, wherein the estimate of the cardiac signal includes the cardiac signal and components of the neural signal, and generating a denoised neural signal based on the estimate of the cardiac signal and a second group of components of the plurality of components of the sensed signal, wherein the cardiac signal is suppressed in the denoised neural signal, and wherein the second group of components excludes the first group of components.

An input dataset is processed to obtain a pre-event window of model inputs and a post-event window of model inputs. The input dataset is a subset of a larger subject-patient dataset that includes different data types and features of the patient. The data types are based on electrical activity of the patient's brain that is sensed and stored by an implanted neurostimulation system. A clinical response estimator (CRE) model is applied to the pre-event window of model inputs to derive pre-event CRE biomarkers. The CRE model is also applied to the post-event window of model inputs to derive post-event CRE biomarkers. The pre-event CRE biomarkers and post-event CRE biomarkers are displayed as a function of time together with the occurrence of the event of interest.

An implanted neurostimulation system is configured to sense episodes of electrographic events and determine durations of episodes. Durations of electrographic events sensed by the implanted neurostimulation system are mapped with seizure probability biomarkers derived from records of the electrographic events to create a mapping function. A seizure probability biomarker that has a value desired for the operation of the implanted neurostimulation system is selected. The duration mapped to the selected seizure probability biomarker is identified and programmed into a control module of the implanted neurostimulation system as a programmed parameter that triggers the operation by the implanted neurostimulation system. The process may be repeated for other operations of the implanted neurostimulation system.

Provided herein are a method of detecting neurological impairment and a device for testing sensorimotor control to detect neurological impairment. The method includes positioning a head-mounted display on the user's head, the head-mounted display placing the user in a virtual or augmented reality environment; presenting the subject with a software-generated object in the virtual or augmented reality environment; providing instructions directing the user to execute one or more sensorimotor activities relating to the software-generated object within the virtual or augmented reality environment; recording data during execution of the one or more objectives; and determining if there is a neurological impairment based upon the recorded data. The device includes a head-mounted display, one or more processors, and one or more hardware storage devices having stored thereon computer-executable instructions which are executable by the one or more processors to display a virtual immersive environment in the head-mounted display.

A stimulating system for collaborative functions of brain and body according to this invention comprising a touch sensor which senses a user's input and transmits the detected input signals when at least one part of user's body touches at least one predefined area of the said sensor, a stimulating pattern generator which contains instruction directing a user to touch the sensor in a predefined pattern, a stimulating pattern display unit which displays the instruction corresponding to the said stimulating pattern for the user, a synergistic operation processor which processes the efficiency in the synergistic operations of brain and body and the stimulating pattern generator wherein the said synergistic operation processor is configured to receive touch data from the said touch sensor, examine a relationship between the said data and the pattern defined by the stimulating pattern generator and process such relationship in the form of a relationship score based on the predefined levels of relationship.

An illustrative optical measurement system includes a light source configured to emit light directed at a target. The system further includes a detector configured to detect arrival times for photons of the light after the light is scattered by the target. The system further includes a temperature sensor configured to output a temperature signal representative of a temperature of the light source. The system further includes an optical sensor configured to output a power signal representative of an optical power level of the light emitted by the light source. The system further includes a driver circuit configured to output, based on the temperature signal and the power signal, an input current for the light source.

An illustrative system includes a brain interface system configured to be worn by a user and to output brain activity data associated with the user; a sleep tracking device configured to be worn by the user and to output sleep tracking data associated with the user; and a computing device configured to generate, based on the brain activity data and the sleep tracking data, sleep routine data representative of a target sleep routine for the user.

A non-invasive self-autonomous system and method of optimizing a lifestyle regimen of a person containing a combination of lifestyle variables is provided. At least one value of the combination of lifestyle variables is repeatedly modified, thereby creating different variations of the combination of lifestyle variables respectively having different sets of values. The different variations of the combination of lifestyle variables are sequentially administered to the person. Physiological activity of the person is detected in response to the administration of the combination of lifestyle variables to the person. Sets of qualitative indicators of an aspect of a lifestyle of the person are derived from the detected physiological activity of the person. The lifestyle regimen of the person is optimized based on the different variations of the combination of lifestyle variables and the derived sets of qualitative indicators.

Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having autonomous or semi-autonomous operation features are provided. According to certain aspects, a computer-implemented method for generating or updating usage-based insurance policies for autonomous or semi-autonomous vehicles may be provided. A request to generate an insurance quote may be received via wireless communication, and with the customer's permission, risk levels associated with intended usage by the customer of an autonomous or semi-autonomous vehicle may be determined. An insurance policy may be adjusted based upon the risk levels and the intended vehicle usage. The insurance policy may then be presented on the customer's mobile device for review and approval. In some aspects, the vehicle may be rented, and the intended vehicle usage is measured in distance or duration of vehicle operation. Insurance discounts may be provided to risk averse vehicle owners based upon low risk levels.

An earpiece module includes a physiological sensor, an external energy sensor, a transceiver, a communication module, a data storage component, and a power source. The communication module includes a microphone, a speaker, and a signal processor. The signal processor processes audio information received from a remote source via the transceiver and communicates the processed audio information to a subject via the speaker. The signal processor processes information in real time from the physiological sensor and the external energy sensor, and the signal processor provides biofeedback to the subject based on signals produced by the physiological sensor. The data storage component includes a plurality of algorithms. At least one algorithm focuses processing resources on extracting physiological information from the physiological sensor, at least one algorithm is configured to be modified or uploaded wirelessly via the transceiver, and at least one algorithm is a compression/decompression (CODEC) algorithm.